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Author

Satyajit Kumar

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Open access Sep 2026

An Interpretable Workflow for Cohort Phenotyping Using Longitudinal Wearable Data.

This work introduces an interpretable workflow for cohort-level phenotyping that leverages longitudinal timestamped wearable metrics, clinically relevant risk strata, and unsupervised machine learning methods to improve transparency and cross-study comparability, supports clinically interpretable subgroup discovery, an...

Ziv Lautman, Chi Chang, E. Rangan et al. · 0 citations
Jul 2026

A Controlled Study of Attention-Only Transformers

This work pretrain attention-only decoder transformers against standard transformers matched separately for parameter count, training FLOPs, and depth (2 to 48 layers), for up to 105B tokens at 6M to 87M parameters, and localizes the remaining gap to parametric recall.

Henry Ndubuaku, Karen Mosoyan, Jakub Mroz et al. · 1 citation

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